Algorithmic trading includes collecting market data, applying an algorithm to the data to generate a forecast of financial market activity, and executing a trade based on the forecast. Algorithmic trading requires real-time processing and analysis of large amounts of financial data. However, the financial services industry struggles with absorbing large quantities of market data and running algorithms to forecast financial activity. Currently, there is a substantial time lag between the receipt of the market data and the generation of an algorithmic forecast of the financial marketplace. As a result, by the time the algorithm makes a forecast, the market has moved, rendering the forecast obsolete.
In many systems, a single machine, with a sole processor, both collects and processes the market data and executes algorithms on the processed data. Because these single machine systems lack the memory and processor speed necessary to take in and output large volumes of data in real time without a substantial time lag, these single machine systems cannot perform real time financial forecasting.
In one example of a single machine system, one computer receives market data and performs the algorithmic computations. However, this single computer is not able to perform real time algorithmic analysis of market data due to the limited speed of its processor. That is, by the time the single computer has processed all of the market data and generated a forecast, the market has already moved and the forecast is stale.
Even systems that are able to perform some real time analysis and forecasting, lose the ability to do so for extremely large quantities of data. For example, a system may be able to process 1000 megabytes (“megs”) of market data in 10 milliseconds (“ms”). However, this system would not be able to process 8000 megs of market data in 10 ms.
Efforts to alleviate the time lags in algorithmic trading include locating the servers collecting the market data and generating the forecasts in close physical proximity to the sources of the market data, such as stock exchanges. For example, a server may be located across the street from a stock exchange in an effort to decrease the processing time of market data and thus reduce the time lag.